The mortality rate for lung cancer is higher than that for other kinds of cancers around the world. Detection of suspicious lesions in the early stages of cancer can be considered the most effective way to improve survival. Nodule detection is one of the more challenging tasks in medical imaging. Nodules may be difficult to detect on CT scans because of low contrast, small size, or location of the nodule within an area of complicated anatomy.
Computer-assisted techniques have been proposed to identify regions of interest containing a nodule within a CT scan image, to segment the nodule from surrounding objects such as blood vessels or the lung wall, to calculate physical characteristics of the nodule, and/or to provide an automated diagnosis of the nodule. Fully automated techniques perform all of these steps without intervention by a radiologist, but one or more of these steps may require input from the radiologist, in which case the method may be described as semi-automated.
Many lung nodules are approximately spherical, and various techniques have been proposed to identify spherical structures within a CT scan image. For example, the Nodule-Enhanced Viewing algorithm from Siemens AG is believed to perform thresholding on a three-dimensional (3D) CT scan to identify voxels having an intensity between predetermined maximum and minimum values. The identified voxels are grouped into connected objects, and objects which are approximately spherical are highlighted.
U.S. 2003/0099391 discloses a method for automatically segmenting a lung nodule by dynamic programming and expectation maximization (EM), using a deformable circular model to estimate the contour of the nodule in each two-dimensional (2D) slice of the scan image, and fitting a three-dimension (3D) surface to the contours.
U.S. 2003/0167001 discloses a method for automatically segmenting a CT image to identify regions of interest and to detect nodules within the regions of interest, in which a sphere is modeled within the region of interest, and points within the sphere are identified as belonging to a nodule, while a morphological test is applied to regions outside the sphere to determine whether they belong to the nodule.
Although many nodules are approximately spherical, the non-spherical aspects of a nodule may be most important for calculating physical characteristics and for performing diagnosis. A spherical model may be useful to segment nodules from surrounding objects, but if the result is to incorrectly identify the nodule as a sphere and to discard non-spherical portions of the nodule, the characteristics of the nodule may also be incorrectly identified.